
#Loading Data
import os
import pandas as pd

def load_csv(file_name):
    current_dir = os.getcwd()
    file_path = os.path.join(current_dir, file_name)

    df = pd.read_csv(file_path)
    return df

# Replace 'Global_Country_Information_Dataset_2023.csv' with your actual .csv file name
df = load_csv('world-data-2023.csv')
df


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 195 entries, 0 to 194
Data columns (total 35 columns):
 #   Column                                     Non-Null Count  Dtype  
---  ------                                     --------------  -----  
 0   Country                                    195 non-null    object 
 1   Density
(P/Km2)                            195 non-null    object 
 2   Abbreviation                               193 non-null    object 
 3   Agricultural Land( %)                      188 non-null    object 
 4   Land Area(Km2)                             194 non-null    object 
 5   Armed Forces size                          171 non-null    object 
 6   Birth Rate                                 189 non-null    float64
 7   Calling Code                               194 non-null    float64
 8   Capital/Major City                         195 non-null    object 
 9   Co2-Emissions                              188 non-null    object 
 10  CPI                                        178 non-null    object 
 11  CPI Change (%)                             179 non-null    object 
 12  Currency-Code                              180 non-null    object 
 13  Fertility Rate                             188 non-null    float64
 14  Forested Area (%)                          188 non-null    object 
 15  Gasoline Price                             175 non-null    object 
 16  GDP                                        193 non-null    object 
 17  Gross primary education enrollment (%)     188 non-null    object 
 18  Gross tertiary education enrollment (%)    183 non-null    object 
 19  Infant mortality                           189 non-null    float64
 20  Largest city                               189 non-null    object 
 21  Life expectancy                            187 non-null    float64
 22  Maternal mortality ratio                   181 non-null    float64
 23  Minimum wage                               150 non-null    object 
 24  Official language                          190 non-null    object 
 25  Out of pocket health expenditure           188 non-null    object 
 26  Physicians per thousand                    188 non-null    float64
 27  Population                                 194 non-null    object 
 28  Population: Labor force participation (%)  176 non-null    object 
 29  Tax revenue (%)                            169 non-null    object 
 30  Total tax rate                             183 non-null    object 
 31  Unemployment rate                          176 non-null    object 
 32  Urban_population                           190 non-null    object 
 33  Latitude                                   194 non-null    float64
 34  Longitude                                  194 non-null    float64
dtypes: float64(9), object(26)
memory usage: 53.4+ KB


for column in df.columns:
    column.strip()
    
df.rename(columns={'Density\n(P/Km2)': 'Density', 
                'Agricultural Land( %)': 'Agricultural Land (%)',
                'Urban_population': 'Urban population',
                'Co2-Emissions': 'CO2 Emissions',
                'Land Area(Km2)': 'Land Area'}, inplace=True)


df.columns

Index(['Country', 'Density', 'Abbreviation', 'Agricultural Land (%)',
       'Land Area', 'Armed Forces size', 'Birth Rate', 'Calling Code',
       'Capital/Major City', 'CO2 Emissions', 'CPI', 'CPI Change (%)',
       'Currency-Code', 'Fertility Rate', 'Forested Area (%)',
       'Gasoline Price', 'GDP', 'Gross primary education enrollment (%)',
       'Gross tertiary education enrollment (%)', 'Infant mortality',
       'Largest city', 'Life expectancy', 'Maternal mortality ratio',
       'Minimum wage', 'Official language', 'Out of pocket health expenditure',
       'Physicians per thousand', 'Population',
       'Population: Labor force participation (%)', 'Tax revenue (%)',
       'Total tax rate', 'Unemployment rate', 'Urban population', 'Latitude',
       'Longitude'],
      dtype='object')


numerical_columns = [
    'Density', 'Agricultural Land (%)', 'Land Area', 'Armed Forces size', 'Birth Rate',
    'CO2 Emissions', 'CPI', 'CPI Change (%)', 'Fertility Rate', 'Forested Area (%)',
    'Gasoline Price', 'GDP', 'Gross primary education enrollment (%)',
    'Gross tertiary education enrollment (%)', 'Infant mortality', 'Life expectancy',
    'Maternal mortality ratio', 'Minimum wage', 'Out of pocket health expenditure',
    'Physicians per thousand', 'Population', 'Population: Labor force participation (%)',
    'Tax revenue (%)', 'Total tax rate', 'Unemployment rate', 'Urban population', 'Latitude',
    'Longitude'
]


for column in numerical_columns:
    df[column] = df[column].astype(str)
    df[column] = df[column].str.replace(",","").str.replace("$","").str.replace("%","")
    df[column] = df[column].astype(float)


df_codes = load_csv('country_codes.csv')
df_codes


merged_df = pd.merge(df, df_codes[['alpha-2', 'alpha-3', 'region', 'sub-region']], left_on='Abbreviation', right_on='alpha-2', how='inner')

merged_df


import plotly.graph_objects as go
import plotly.express as px
import json
import pandas as pd
import numpy as np


fig = go.Figure(
    go.Choropleth(locations=merged_df['alpha-3'],
            z=merged_df['Population'],
            colorscale='Reds',
            text = merged_df['Country'],
            colorbar_title = 'Population',
            marker_line_width=0.5
                    ))

fig.update_geos(projection_type='natural earth')

fig.update_layout(title='<b>Countries by population.</b>',
    height=300,
    margin={"r":40,"t":40,"l":40,"b":0})

fig.show()


european_df = merged_df[merged_df['region'] == 'Europe'].copy()

fig = go.Figure(
    go.Choropleth(locations=european_df['alpha-3'],
            z=european_df['Population'],
            colorscale='Reds',
            reversescale=False,
            text = european_df['Country'],
            colorbar_title = 'Population',
            marker_line_width=0.5
                    ))

fig.update_geos(scope='europe',resolution=50)
fig.update_geos(projection_type='natural earth')

fig.update_layout(title='<b>Countries by population.</b>',
    height=300,
    margin={"r":40,"t":40,"l":40,"b":0})

fig.show()


asia_df = merged_df[merged_df['region'] == 'Asia'].copy()

fig = go.Figure(
    go.Choropleth(locations = asia_df['alpha-3'],
            z = asia_df['Population'],
            colorscale = 'Reds',
            reversescale = False,
            text = asia_df['Country'],
            colorbar_title = 'Population',
            marker_line_width=0.5
                    ))

fig.update_geos(scope='asia',resolution=50)
fig.update_geos(projection_type='natural earth')

fig.update_layout(title='<b>Countries by population.</b>',
    height=300,
    margin={"r":40,"t":40,"l":40,"b":0})

fig.show()


america_df = merged_df[merged_df['region'] == 'Americas'].copy()

fig = go.Figure(
    go.Choropleth(locations = america_df['alpha-3'],
            z = america_df['Population'],
            colorscale = 'Reds',
            reversescale = False,
            text = america_df['Country'],
            colorbar_title = 'Population',
            marker_line_width=0.5
                    ))

fig.update_geos(scope='north america',resolution=50)
fig.update_geos(projection_type='natural earth')

fig.update_layout(title='<b>Countries by population.</b>',
    height=300,
    margin={"r":40,"t":40,"l":40,"b":0})

fig.show()


merged_df['sub-region'].unique()

array(['Southern Asia', 'Southern Europe', 'Northern Africa',
       'Sub-Saharan Africa', 'Latin America and the Caribbean',
       'Western Asia', 'Australia and New Zealand', 'Western Europe',
       'Eastern Europe', 'South-eastern Asia', 'Northern America',
       'Eastern Asia', 'Northern Europe', 'Melanesia', 'Central Asia',
       'Micronesia', 'Polynesia'], dtype=object)


south_america_df = merged_df[merged_df['sub-region'] == 'Latin America and the Caribbean'].copy()

fig = go.Figure(
    go.Choropleth(locations = south_america_df['alpha-3'],
            z = south_america_df['Population'],
            colorscale = 'Reds',
            reversescale = False,
            text = south_america_df['Country'],
            colorbar_title = 'Population',
            marker_line_width=0.5
                    ))

fig.update_geos(scope='south america',resolution=50)
fig.update_geos(projection_type='natural earth')

fig.update_layout(title='<b>Countries by population.</b>',
    height=300,
    margin={"r":40,"t":40,"l":40,"b":0})

fig.show()


df_capitals = load_csv('country_capitals.csv')
df_capitals


merged_df_2 = pd.merge(df, df_capitals[['latitude', 'longitude', 'countryCode']], left_on='Abbreviation', right_on='countryCode', how='inner')

merged_df_2


fig = go.Figure(data=go.Scattergeo(
        lon = merged_df_2['longitude'],
        lat = merged_df_2['latitude'],
        mode = 'markers',
        name='',
        customdata=merged_df_2['Capital/Major City'],
        hovertext=merged_df_2['Country'],
        hovertemplate='Country: %{hovertext}<br>Capital: %{customdata}',
        marker = dict(
            size = 5,
            opacity = 0.8,
            reversescale = True,
            autocolorscale = False,
            color='#fee08b',
            symbol = 'circle',
            line = dict(
                width=1,
                color='#000000'
            ),
        )))
fig.update_geos(scope='world',
    #lonaxis_range=[ -20.0, 37.0 ],
    #lataxis_range=[ 30.0, 60.0 ],
    #center={'lat': 51.919438, 'lon': 19.145136},
    resolution=50,
    landcolor='#f0f0f0',
    countrycolor='#000000',
    coastlinecolor='#000000')
fig.update_layout(title=dict(
        text='<b>Capitals</b>',
        xanchor='center',
        x=.5),
    margin={"r":0,"t":50,"l":0,"b":0},
)
fig.show()


# Wyklucz konkretne państwa
excluded_countries = ["Morocco", "Algeria", "Tunisia", "Turkey", "Palestinian National Authority",
                      "Lebanon", "Syria", "Iraq", "Armenia", "Georgia", "Azerbaijan",
                     "Jordan", "Israel", "Namibia", "Eswatini"]


filtered_merged_df = merged_df_2.query("Country not in @excluded_countries").copy()


fig = go.Figure(data=go.Scattergeo(
        lon = filtered_merged_df['longitude'],
        lat = filtered_merged_df['latitude'],
        mode = 'markers',
        name='',
        customdata=filtered_merged_df['Capital/Major City'],
        hovertext=filtered_merged_df['Country'],
        hovertemplate='Country: %{hovertext}<br>Capital: %{customdata}',
        marker = dict(
            size = 8,
            opacity = 0.8,
            reversescale = True,
            autocolorscale = False,
            color='#fee08b',
            symbol = 'circle',
            line = dict(
                width=1,
                color='#000000'
            ),
        )))
fig.update_geos(scope='europe',
    lonaxis_range=[ -20.0, 37.0 ],
    lataxis_range=[ 30.0, 60.0 ],
    center={'lat': 51.919438, 'lon': 19.145136},
    resolution=50,
    landcolor='#f0f0f0',
    countrycolor='#000000',
    coastlinecolor='#000000')
fig.update_layout(title=dict(
        text='<b>Capitals of Europe</b>',
        xanchor='center',
        x=.5),
    margin={"r":0,"t":50,"l":0,"b":0},
)
fig.show()


import plotly.express as px

# Select the top 10 countries with highest CO2 emissions
top_10_co2_emissions_df = df.nlargest(10, columns='CO2 Emissions')

# Create the bar chart for the top 10 countries with highest CO2 Emissions
fig_top_10_emissions = px.bar(
    top_10_co2_emissions_df,
    x='Country',
    y='CO2 Emissions',
    title='Top 10 Countries with Highest CO2 Emissions',
    labels={'CO2 Emissions': 'CO2 Emissions'},
)

# Show the interactive bar chart
fig_top_10_emissions.show()


# Calculate CO2 Emissions per Population for all countries
df['CO2 Emissions per Population'] = df['CO2 Emissions'] / df['Population']

# Sort the DataFrame by CO2 Emissions per Population in descending order and select the top 10 countries
top_10_co2_per_pop_df = df.nlargest(10, columns='CO2 Emissions per Population')

fig_top_10 = px.bar(
    top_10_co2_per_pop_df,
    x='Country',
    y='CO2 Emissions per Population',
    title='Countries with Highest CO2 Emissions per Population',
    labels={'CO2 Emissions per Population': 'CO2 Emissions per Population'},
)

# Sort the DataFrame by CO2 Emissions per Population in ascending order and select the bottom 10 countries
bottom_10_co2_per_pop_df = df.nsmallest(10, columns='CO2 Emissions per Population')

fig_bottom_10 = px.bar(
    bottom_10_co2_per_pop_df,
    x='Country',
    y='CO2 Emissions per Population',
    title='Countries with Lowest CO2 Emissions per Population',
    labels={'CO2 Emissions per Population': 'CO2 Emissions per Population'},
)

# Show the interactive bar charts
fig_top_10.show()
fig_bottom_10.show()

	Country	Density\n(P/Km2)	Abbreviation	Agricultural Land( %)	Land Area(Km2)	Armed Forces size	Birth Rate	Calling Code	Capital/Major City	Co2-Emissions	...	Out of pocket health expenditure	Physicians per thousand	Population	Population: Labor force participation (%)	Tax revenue (%)	Total tax rate	Unemployment rate	Urban_population	Latitude	Longitude
0	Afghanistan	60	AF	58.10%	652,230	323,000	32.49	93.0	Kabul	8,672	...	78.40%	0.28	38,041,754	48.90%	9.30%	71.40%	11.12%	9,797,273	33.939110	67.709953
1	Albania	105	AL	43.10%	28,748	9,000	11.78	355.0	Tirana	4,536	...	56.90%	1.20	2,854,191	55.70%	18.60%	36.60%	12.33%	1,747,593	41.153332	20.168331
2	Algeria	18	DZ	17.40%	2,381,741	317,000	24.28	213.0	Algiers	150,006	...	28.10%	1.72	43,053,054	41.20%	37.20%	66.10%	11.70%	31,510,100	28.033886	1.659626
3	Andorra	164	AD	40.00%	468	NaN	7.20	376.0	Andorra la Vella	469	...	36.40%	3.33	77,142	NaN	NaN	NaN	NaN	67,873	42.506285	1.521801
4	Angola	26	AO	47.50%	1,246,700	117,000	40.73	244.0	Luanda	34,693	...	33.40%	0.21	31,825,295	77.50%	9.20%	49.10%	6.89%	21,061,025	-11.202692	17.873887
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
190	Venezuela	32	VE	24.50%	912,050	343,000	17.88	58.0	Caracas	164,175	...	45.80%	1.92	28,515,829	59.70%	NaN	73.30%	8.80%	25,162,368	6.423750	-66.589730
191	Vietnam	314	VN	39.30%	331,210	522,000	16.75	84.0	Hanoi	192,668	...	43.50%	0.82	96,462,106	77.40%	19.10%	37.60%	2.01%	35,332,140	14.058324	108.277199
192	Yemen	56	YE	44.60%	527,968	40,000	30.45	967.0	Sanaa	10,609	...	81.00%	0.31	29,161,922	38.00%	NaN	26.60%	12.91%	10,869,523	15.552727	48.516388
193	Zambia	25	ZM	32.10%	752,618	16,000	36.19	260.0	Lusaka	5,141	...	27.50%	1.19	17,861,030	74.60%	16.20%	15.60%	11.43%	7,871,713	-13.133897	27.849332
194	Zimbabwe	38	ZW	41.90%	390,757	51,000	30.68	263.0	Harare	10,983	...	25.80%	0.21	14,645,468	83.10%	20.70%	31.60%	4.95%	4,717,305	-19.015438	29.154857

	name	alpha-2	alpha-3	country-code	iso_3166-2	region	sub-region	intermediate-region	region-code	sub-region-code	intermediate-region-code
0	Afghanistan	AF	AFG	4	ISO 3166-2:AF	Asia	Southern Asia	NaN	142.0	34.0	NaN
1	Åland Islands	AX	ALA	248	ISO 3166-2:AX	Europe	Northern Europe	NaN	150.0	154.0	NaN
2	Albania	AL	ALB	8	ISO 3166-2:AL	Europe	Southern Europe	NaN	150.0	39.0	NaN
3	Algeria	DZ	DZA	12	ISO 3166-2:DZ	Africa	Northern Africa	NaN	2.0	15.0	NaN
4	American Samoa	AS	ASM	16	ISO 3166-2:AS	Oceania	Polynesia	NaN	9.0	61.0	NaN
...	...	...	...	...	...	...	...	...	...	...	...
244	Wallis and Futuna	WF	WLF	876	ISO 3166-2:WF	Oceania	Polynesia	NaN	9.0	61.0	NaN
245	Western Sahara	EH	ESH	732	ISO 3166-2:EH	Africa	Northern Africa	NaN	2.0	15.0	NaN
246	Yemen	YE	YEM	887	ISO 3166-2:YE	Asia	Western Asia	NaN	142.0	145.0	NaN
247	Zambia	ZM	ZMB	894	ISO 3166-2:ZM	Africa	Sub-Saharan Africa	Eastern Africa	2.0	202.0	14.0
248	Zimbabwe	ZW	ZWE	716	ISO 3166-2:ZW	Africa	Sub-Saharan Africa	Eastern Africa	2.0	202.0	14.0

	Country	Density	Abbreviation	Agricultural Land (%)	Land Area	Armed Forces size	Birth Rate	Calling Code	Capital/Major City	CO2 Emissions	...	Tax revenue (%)	Total tax rate	Unemployment rate	Urban population	Latitude	Longitude	alpha-2	alpha-3	region	sub-region
0	Afghanistan	60.0	AF	58.1	652230.0	323000.0	32.49	93.0	Kabul	8672.0	...	9.3	71.4	11.12	9797273.0	33.939110	67.709953	AF	AFG	Asia	Southern Asia
1	Albania	105.0	AL	43.1	28748.0	9000.0	11.78	355.0	Tirana	4536.0	...	18.6	36.6	12.33	1747593.0	41.153332	20.168331	AL	ALB	Europe	Southern Europe
2	Algeria	18.0	DZ	17.4	2381741.0	317000.0	24.28	213.0	Algiers	150006.0	...	37.2	66.1	11.70	31510100.0	28.033886	1.659626	DZ	DZA	Africa	Northern Africa
3	Andorra	164.0	AD	40.0	468.0	NaN	7.20	376.0	Andorra la Vella	469.0	...	NaN	NaN	NaN	67873.0	42.506285	1.521801	AD	AND	Europe	Southern Europe
4	Angola	26.0	AO	47.5	1246700.0	117000.0	40.73	244.0	Luanda	34693.0	...	9.2	49.1	6.89	21061025.0	-11.202692	17.873887	AO	AGO	Africa	Sub-Saharan Africa
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
190	Venezuela	32.0	VE	24.5	912050.0	343000.0	17.88	58.0	Caracas	164175.0	...	NaN	73.3	8.80	25162368.0	6.423750	-66.589730	VE	VEN	Americas	Latin America and the Caribbean
191	Vietnam	314.0	VN	39.3	331210.0	522000.0	16.75	84.0	Hanoi	192668.0	...	19.1	37.6	2.01	35332140.0	14.058324	108.277199	VN	VNM	Asia	South-eastern Asia
192	Yemen	56.0	YE	44.6	527968.0	40000.0	30.45	967.0	Sanaa	10609.0	...	NaN	26.6	12.91	10869523.0	15.552727	48.516388	YE	YEM	Asia	Western Asia
193	Zambia	25.0	ZM	32.1	752618.0	16000.0	36.19	260.0	Lusaka	5141.0	...	16.2	15.6	11.43	7871713.0	-13.133897	27.849332	ZM	ZMB	Africa	Sub-Saharan Africa
194	Zimbabwe	38.0	ZW	41.9	390757.0	51000.0	30.68	263.0	Harare	10983.0	...	20.7	31.6	4.95	4717305.0	-19.015438	29.154857	ZW	ZWE	Africa	Sub-Saharan Africa

	latitude	longitude	countryCode	continent
0	-54.283333	-36.500000	GS	Antarctica
1	-49.350000	70.216667	TF	Antarctica
2	31.766667	35.233333	PS	Asia
3	60.116667	19.900000	AX	Europe
4	-0.547700	166.920867	NR	Australia
...	...	...	...	...
239	35.183333	33.366667	NaN	Europe
240	22.285000	114.158000	HK	Asia
241	0.000000	0.000000	HM	Antarctica
242	-7.300000	72.400000	IO	Africa
243	0.000000	0.000000	MO	Asia

	Country	Density	Abbreviation	Agricultural Land (%)	Land Area	Armed Forces size	Birth Rate	Calling Code	Capital/Major City	CO2 Emissions	...	Population: Labor force participation (%)	Tax revenue (%)	Total tax rate	Unemployment rate	Urban population	Latitude	Longitude	latitude	longitude	countryCode
0	Afghanistan	60.0	AF	58.1	652230.0	323000.0	32.49	93.0	Kabul	8672.0	...	48.9	9.3	71.4	11.12	9797273.0	33.939110	67.709953	34.516667	69.183333	AF
1	Albania	105.0	AL	43.1	28748.0	9000.0	11.78	355.0	Tirana	4536.0	...	55.7	18.6	36.6	12.33	1747593.0	41.153332	20.168331	41.316667	19.816667	AL
2	Algeria	18.0	DZ	17.4	2381741.0	317000.0	24.28	213.0	Algiers	150006.0	...	41.2	37.2	66.1	11.70	31510100.0	28.033886	1.659626	36.750000	3.050000	DZ
3	Andorra	164.0	AD	40.0	468.0	NaN	7.20	376.0	Andorra la Vella	469.0	...	NaN	NaN	NaN	NaN	67873.0	42.506285	1.521801	42.500000	1.516667	AD
4	Angola	26.0	AO	47.5	1246700.0	117000.0	40.73	244.0	Luanda	34693.0	...	77.5	9.2	49.1	6.89	21061025.0	-11.202692	17.873887	-8.833333	13.216667	AO
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
192	Venezuela	32.0	VE	24.5	912050.0	343000.0	17.88	58.0	Caracas	164175.0	...	59.7	NaN	73.3	8.80	25162368.0	6.423750	-66.589730	10.483333	-66.866667	VE
193	Vietnam	314.0	VN	39.3	331210.0	522000.0	16.75	84.0	Hanoi	192668.0	...	77.4	19.1	37.6	2.01	35332140.0	14.058324	108.277199	21.033333	105.850000	VN
194	Yemen	56.0	YE	44.6	527968.0	40000.0	30.45	967.0	Sanaa	10609.0	...	38.0	NaN	26.6	12.91	10869523.0	15.552727	48.516388	15.350000	44.200000	YE
195	Zambia	25.0	ZM	32.1	752618.0	16000.0	36.19	260.0	Lusaka	5141.0	...	74.6	16.2	15.6	11.43	7871713.0	-13.133897	27.849332	-15.416667	28.283333	ZM
196	Zimbabwe	38.0	ZW	41.9	390757.0	51000.0	30.68	263.0	Harare	10983.0	...	83.1	20.7	31.6	4.95	4717305.0	-19.015438	29.154857	-17.816667	31.033333	ZW

PROJEKT - PRZYGOTOWANIE DANYCH I WIZUALIZACJA¶

Wczytywanie danych¶

Informacje o danych¶

Podsumowanie informacji o danych¶

Przetwarzanie kolumn¶

Usuwanie białych znaków z nazw kolumn¶

Podsumowanie zmienionych nazw kolumn¶

Przetwarzanie danych liczbowych¶

Lista kolumn z danymi liczbowymi¶

Konwersja typu danych i usuwanie zbędnych znaków¶

Łączenie danych o państwach z kodami ISO¶

Wczytywanie danych o kodach ISO¶

Łączenie danych¶

Podsumowanie¶

Tworzenie mapy Choropleth¶

Tworzenie obiektu `Figure`¶

Dodawanie mapy Choropleth¶

Możliwości filtrowania mapy¶

Dodawanie współrzędnych geograficznych stolic państw i wyrysowanie ich na mapie¶

Utworzenie obiektu `Figure` z Scattergeo¶

Wyrysowanie współrzędnych stolic na mapie¶

Konfiguracja mapy¶

Ustawienie tytułu i wyświetlenie mapy¶

Filtrowanie mapy - europejskie stolice¶

Tworzenie wykresu słupkowego dla 10 krajów o najwyższych emisjach CO2¶

Wybór 10 krajów z największymi emisjami CO2¶

Tworzenie wykresu słupkowego¶

Obliczanie emisji CO2 na mieszkańca¶

Tworzenie wykresu słupkowego dla 10 krajów o najwyższej emisji CO2 na mieszkańca¶

Tworzenie wykresu słupkowego dla 10 krajów o najniższej emisji CO2 na mieszkańca¶

Podsumowanie¶

Wczytywanie danych z plików CSV¶

Łączenie danych z różnych źródeł¶

Manipulacja danymi w Pandas DataFrame¶

Wizualizacja danych na mapach¶

Tworzenie wykresów słupkowych¶

Zakończenie¶

PROJEKT - PRZYGOTOWANIE DANYCH I WIZUALIZACJA¶

Wczytywanie danych¶

Informacje o danych¶

Podsumowanie informacji o danych¶

Przetwarzanie kolumn¶

Usuwanie białych znaków z nazw kolumn¶

Podsumowanie zmienionych nazw kolumn¶

Przetwarzanie danych liczbowych¶

Lista kolumn z danymi liczbowymi¶

Konwersja typu danych i usuwanie zbędnych znaków¶

Łączenie danych o państwach z kodami ISO¶

Wczytywanie danych o kodach ISO¶

Łączenie danych¶

Podsumowanie¶

Tworzenie mapy Choropleth¶

Tworzenie obiektu Figure¶

Dodawanie mapy Choropleth¶

Możliwości filtrowania mapy¶

Dodawanie współrzędnych geograficznych stolic państw i wyrysowanie ich na mapie¶

Utworzenie obiektu Figure z Scattergeo¶

Wyrysowanie współrzędnych stolic na mapie¶

Konfiguracja mapy¶

Ustawienie tytułu i wyświetlenie mapy¶

Filtrowanie mapy - europejskie stolice¶

Tworzenie wykresu słupkowego dla 10 krajów o najwyższych emisjach CO2¶

Wybór 10 krajów z największymi emisjami CO2¶

Tworzenie wykresu słupkowego¶

Obliczanie emisji CO2 na mieszkańca¶

Tworzenie wykresu słupkowego dla 10 krajów o najwyższej emisji CO2 na mieszkańca¶

Tworzenie wykresu słupkowego dla 10 krajów o najniższej emisji CO2 na mieszkańca¶

Podsumowanie¶

Wczytywanie danych z plików CSV¶

Łączenie danych z różnych źródeł¶

Manipulacja danymi w Pandas DataFrame¶

Wizualizacja danych na mapach¶

Tworzenie wykresów słupkowych¶

Zakończenie¶

Tworzenie obiektu `Figure`¶

Utworzenie obiektu `Figure` z Scattergeo¶